added measurement grouping for beacons
had to change the parameter boundaries of the wifi optimizer to be able to use it for bluetooth... this should be refactored to something more generic.. some minor changes in ble
This commit is contained in:
mail@toni-fetzer.de
@@ -37,355 +37,355 @@
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namespace WiFiOptimizer {
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/**
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* optimize access-point parameters,
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* given several fingerprints using the log-dist-ceiling model
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*/
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struct LogDistCeiling : public Base {
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/**
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* optimize access-point parameters,
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* given several fingerprints using the log-dist-ceiling model
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*/
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struct LogDistCeiling : public Base {
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public:
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public:
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/**
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* resulting optimization stats for one AP
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*/
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struct Stats {
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/**
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* resulting optimization stats for one AP
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*/
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struct Stats {
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/** average model<->scan error after optimzing */
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float error_db;
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/** average model<->scan error after optimzing */
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float error_db;
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/** number of fingerprints [= locations] that were used for optimzing */
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int usedFingerprins;
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/** number of fingerprints [= locations] that were used for optimzing */
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int usedFingerprins;
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/** resulting model<->scan error after optimzing for each individual fingerprints [= location] */
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std::vector<ErrorAtPosition> errors;
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/** resulting model<->scan error after optimzing for each individual fingerprints [= location] */
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std::vector<ErrorAtPosition> errors;
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/** get the location where the model estimation reaches the highest negative value [model estimation too low] */
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ErrorAtPosition getEstErrorMaxNeg() const {
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auto cmpErrAtPos = [] (const ErrorAtPosition& a, const ErrorAtPosition& b) {return a.getError_db() < b.getError_db();};
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return *std::min_element(errors.begin(), errors.end(), cmpErrAtPos);
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}
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/** get the location where the model estimation reaches the highest negative value [model estimation too low] */
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ErrorAtPosition getEstErrorMaxNeg() const {
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auto cmpErrAtPos = [] (const ErrorAtPosition& a, const ErrorAtPosition& b) {return a.getError_db() < b.getError_db();};
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return *std::min_element(errors.begin(), errors.end(), cmpErrAtPos);
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}
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/** get the location where the model estimation reaches the highest positive value [model estimation too high] */
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ErrorAtPosition getEstErrorMaxPos() const {
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auto cmpErrAtPos = [] (const ErrorAtPosition& a, const ErrorAtPosition& b) {return a.getError_db() < b.getError_db();};
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return *std::max_element(errors.begin(), errors.end(), cmpErrAtPos);
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}
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/** get the location where the model estimation reaches the highest positive value [model estimation too high] */
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ErrorAtPosition getEstErrorMaxPos() const {
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auto cmpErrAtPos = [] (const ErrorAtPosition& a, const ErrorAtPosition& b) {return a.getError_db() < b.getError_db();};
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return *std::max_element(errors.begin(), errors.end(), cmpErrAtPos);
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}
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};
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};
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using APFilter = std::function<bool(const Stats& stats, const MACAddress& mac)>;
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using APFilter = std::function<bool(const Stats& stats, const MACAddress& mac)>;
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static inline bool NONE(const Stats& stats, const MACAddress& mac) {
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(void) stats; (void) mac;
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return false;
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}
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static inline bool NONE(const Stats& stats, const MACAddress& mac) {
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(void) stats; (void) mac;
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return false;
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}
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static inline bool MIN_2_FPS(const Stats& stats, const MACAddress& mac) {
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(void) mac;
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return stats.usedFingerprins < 2;
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}
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static inline bool MIN_2_FPS(const Stats& stats, const MACAddress& mac) {
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(void) mac;
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return stats.usedFingerprins < 2;
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}
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static inline bool MIN_5_FPS(const Stats& stats, const MACAddress& mac) {
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(void) mac;
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return stats.usedFingerprins < 5;
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}
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static inline bool MIN_5_FPS(const Stats& stats, const MACAddress& mac) {
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(void) mac;
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return stats.usedFingerprins < 5;
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}
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static inline bool MIN_10_FPS(const Stats& stats, const MACAddress& mac) {
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(void) mac;
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return stats.usedFingerprins < 10;
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}
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static inline bool MIN_10_FPS(const Stats& stats, const MACAddress& mac) {
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(void) mac;
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return stats.usedFingerprins < 10;
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}
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/** parameters for one AP when using the LogDistCeiling model */
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struct APParams {
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/** parameters for one AP when using the LogDistCeiling model */
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struct APParams {
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float x;
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float y;
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float z;
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float x;
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float y;
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float z;
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float txp;
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float exp;
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float waf;
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float txp;
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float exp;
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float waf;
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/** ctor */
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APParams() {;}
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/** ctor */
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APParams() {;}
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/** ctor */
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APParams(float x, float y, float z, float txp, float exp, float waf) : x(x), y(y), z(z), txp(txp), exp(exp), waf(waf) {;}
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/** ctor */
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APParams(float x, float y, float z, float txp, float exp, float waf) : x(x), y(y), z(z), txp(txp), exp(exp), waf(waf) {;}
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Point3 getPos() const {return Point3(x,y,z);}
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Point3 getPos() const {return Point3(x,y,z);}
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std::string asString() const {
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std::stringstream ss;
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ss << "Pos:" << getPos().asString() << " TXP:" << txp << " EXP:" << exp << " WAF:" << waf;
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return ss.str();
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}
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std::string asString() const {
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std::stringstream ss;
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ss << "Pos:" << getPos().asString() << " TXP:" << txp << " EXP:" << exp << " WAF:" << waf;
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return ss.str();
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}
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/** we add some constraints to the parameter range */
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bool outOfRange() const {
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return (waf > 0) ||
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(txp < -50) ||
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(txp > -30) ||
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(exp > 4) ||
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(exp < 1);
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}
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/** we add some constraints to the parameter range */
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bool outOfRange() const {
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return (waf > 0) ||
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(txp < -65) ||
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(txp > -45) ||
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(exp > 5) ||
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(exp < 1);
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}
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};
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};
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/** add MAC-info to params */
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struct APParamsMAC {
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MACAddress mac;
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APParams params;
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APParamsMAC(const MACAddress mac, const APParams& params) : mac(mac), params(params) {;}
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};
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/** add MAC-info to params */
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struct APParamsMAC {
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MACAddress mac;
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APParams params;
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APParamsMAC(const MACAddress mac, const APParams& params) : mac(mac), params(params) {;}
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};
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struct OptResultStats {
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K::Statistics<float> avgApErrors; // contains one average-error per optimized AP
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K::Statistics<float> singleFPErrors; // contains one error for each fingerprint<->ap SIGNED
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K::Statistics<float> singleFPErrorsAbs; // contains one error for each fingerprint<->ap ABSOLUTE
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struct OptResultStats {
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K::Statistics<float> avgApErrors; // contains one average-error per optimized AP
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K::Statistics<float> singleFPErrors; // contains one error for each fingerprint<->ap SIGNED
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K::Statistics<float> singleFPErrorsAbs; // contains one error for each fingerprint<->ap ABSOLUTE
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};
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};
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class APParamsList {
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class APParamsList {
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std::vector<APParamsMAC> lst;
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std::vector<APParamsMAC> lst;
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public:
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public:
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/** ctor */
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APParamsList(const std::vector<APParamsMAC>& lst) : lst(lst) {
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/** ctor */
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APParamsList(const std::vector<APParamsMAC>& lst) : lst(lst) {
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}
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}
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/** get the list */
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const std::vector<APParamsMAC>& get() const {
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return lst;
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}
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/** get the list */
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const std::vector<APParamsMAC>& get() const {
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return lst;
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}
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/** get params for the given mac [if known, otherwise nullptr] */
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const APParamsMAC* get (const MACAddress& mac) const {
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for (const APParamsMAC& ap : lst) {
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if (ap.mac == mac) {return ≈}
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}
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return nullptr;
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}
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/** get params for the given mac [if known, otherwise nullptr] */
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const APParamsMAC* get (const MACAddress& mac) const {
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for (const APParamsMAC& ap : lst) {
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if (ap.mac == mac) {return ≈}
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}
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return nullptr;
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}
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};
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};
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private:
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private:
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Floorplan::IndoorMap* map;
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Floorplan::IndoorMap* map;
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Mode mode = Mode::QUALITY;
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Mode mode = Mode::QUALITY;
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const char* name = "WiFiOptLDC";
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const char* name = "WiFiOptLDC";
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public:
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public:
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/** ctor */
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LogDistCeiling(Floorplan::IndoorMap* map, const VAPGrouper& vg, const Mode mode = Mode::QUALITY) : Base(vg), map(map), mode(mode) {
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;
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}
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/** ctor */
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LogDistCeiling(Floorplan::IndoorMap* map, const VAPGrouper& vg, const Mode mode = Mode::MEDIUM) : Base(vg), map(map), mode(mode) {
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;
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}
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/** ctor */
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LogDistCeiling(Floorplan::IndoorMap* map, const VAPGrouper& vg, const WiFiFingerprints& fps, const Mode mode = Mode::QUALITY) : Base(vg), map(map), mode(mode) {
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addFingerprints(fps);
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}
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/** ctor */
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LogDistCeiling(Floorplan::IndoorMap* map, const VAPGrouper& vg, const WiFiFingerprints& fps, const Mode mode = Mode::MEDIUM) : Base(vg), map(map), mode(mode) {
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addFingerprints(fps);
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}
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/** optimize all known APs */
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APParamsList optimizeAll(APFilter filter, OptResultStats* dst = nullptr) const {
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/** optimize all known APs */
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APParamsList optimizeAll(APFilter filter, OptResultStats* dst = nullptr) const {
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// sanity check
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Assert::isFalse(getAllMACs().empty(), "no APs found for optimization! call addFingerprint() first!");
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// sanity check
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Assert::isFalse(getAllMACs().empty(), "no APs found for optimization! call addFingerprint() first!");
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K::Statistics<float> avgErrors;
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K::Statistics<float> singleErrors;
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K::Statistics<float> singleErrorsAbs;
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K::Statistics<float> avgErrors;
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K::Statistics<float> singleErrors;
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K::Statistics<float> singleErrorsAbs;
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std::vector<APParamsMAC> res;
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for (const MACAddress& mac : getAllMACs()) {
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std::vector<APParamsMAC> res;
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for (const MACAddress& mac : getAllMACs()) {
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// perform optimization, get resulting parameters and optimization stats
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Stats stats;
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const APParams params = optimize(mac, stats);
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// perform optimization, get resulting parameters and optimization stats
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Stats stats;
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const APParams params = optimize(mac, stats);
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// filter based on stats (option to ignore/filter some access-points)
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if (!filter(stats, mac)) {
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res.push_back(APParamsMAC(mac, params));
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//errSum += stats.error_db;
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//++errCnt;
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avgErrors.add(stats.error_db);
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for (const auto e : stats.errors) {
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singleErrors.add(e.getError_db());
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singleErrorsAbs.add(std::abs(e.getError_db()));
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}
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} else {
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Log::add(name, "ignoring opt-result for AP " + mac.asString() + " due to filter");
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//std::cout << "ignored due to filter!" << std::endl;
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}
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// filter based on stats (option to ignore/filter some access-points)
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if (!filter(stats, mac)) {
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res.push_back(APParamsMAC(mac, params));
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//errSum += stats.error_db;
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//++errCnt;
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avgErrors.add(stats.error_db);
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for (const auto e : stats.errors) {
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singleErrors.add(e.getError_db());
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singleErrorsAbs.add(std::abs(e.getError_db()));
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}
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} else {
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Log::add(name, "ignoring opt-result for AP " + mac.asString() + " due to filter");
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//std::cout << "ignored due to filter!" << std::endl;
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}
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}
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}
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//const float avgErr = errSum / errCnt;
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//Log::add(name, "optimized APs: " + std::to_string(errCnt));
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//Log::add(name, "average AP error is: " + std::to_string(avgErr) + " dB");
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Log::add(name, "optimization result: ");
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Log::add(name, " - AvgPerAP " + avgErrors.asString());
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Log::add(name, " - Single: " + singleErrors.asString());
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Log::add(name, " - SingleAbs: " + singleErrorsAbs.asString());
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//const float avgErr = errSum / errCnt;
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//Log::add(name, "optimized APs: " + std::to_string(errCnt));
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//Log::add(name, "average AP error is: " + std::to_string(avgErr) + " dB");
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Log::add(name, "optimization result: ");
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Log::add(name, " - AvgPerAP " + avgErrors.asString());
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Log::add(name, " - Single: " + singleErrors.asString());
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Log::add(name, " - SingleAbs: " + singleErrorsAbs.asString());
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if (dst) {
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dst->avgApErrors = avgErrors;
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dst->singleFPErrors = singleErrors;
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dst->singleFPErrorsAbs = singleErrorsAbs;
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}
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if (dst) {
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dst->avgApErrors = avgErrors;
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dst->singleFPErrors = singleErrors;
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dst->singleFPErrorsAbs = singleErrorsAbs;
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}
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// done
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return APParamsList(res);
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// done
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return APParamsList(res);
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}
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}
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/** optimize the given AP */
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APParams optimize(const MACAddress& mac, Stats& res) const {
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/** optimize the given AP */
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APParams optimize(const MACAddress& mac, Stats& res) const {
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// starting parameters do not matter for the current optimizer!
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APParams params(0,0,0, -40, 2.5, -4.0);
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// starting parameters do not matter for the current optimizer!
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APParams params(0,0,0, -59, 3, -8.0);
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// get all position->rssi measurements for this AP to compare them with the corresponding model estimations
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const std::vector<RSSIatPosition>& entries = apMap.find(mac)->second;
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// get all position->rssi measurements for this AP to compare them with the corresponding model estimations
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const std::vector<RSSIatPosition>& entries = apMap.find(mac)->second;
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// log
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Log::add(name, "optimizing parameters for AP " + mac.asString() + " by using " + std::to_string(entries.size()) + " fingerprints", false);
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Log::tick();
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// log
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Log::add(name, "optimizing parameters for AP " + mac.asString() + " by using " + std::to_string(entries.size()) + " fingerprints", false);
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Log::tick();
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// get the map's size
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const BBox3 mapBBox = FloorplanHelper::getBBox(map);
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// get the map's size
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const BBox3 mapBBox = FloorplanHelper::getBBox(map);
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using LeOpt = K::NumOptAlgoRangeRandom<float>;
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const std::vector<LeOpt::MinMax> valRegion = {
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LeOpt::MinMax(mapBBox.getMin().x - 20, mapBBox.getMax().x + 20), // x
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LeOpt::MinMax(mapBBox.getMin().y - 20, mapBBox.getMax().y + 20), // y
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LeOpt::MinMax(mapBBox.getMin().z - 6, mapBBox.getMax().z + 6), // z
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LeOpt::MinMax(-50, -30), // txp
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LeOpt::MinMax(1, 4), // exp
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LeOpt::MinMax(-15, -0), // waf
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};
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using LeOpt = K::NumOptAlgoRangeRandom<float>;
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const std::vector<LeOpt::MinMax> valRegion = {
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LeOpt::MinMax(mapBBox.getMin().x - 20, mapBBox.getMax().x + 20), // x
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LeOpt::MinMax(mapBBox.getMin().y - 20, mapBBox.getMax().y + 20), // y
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LeOpt::MinMax(mapBBox.getMin().z - 6, mapBBox.getMax().z + 6), // z
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LeOpt::MinMax(-65, -45), // txp
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LeOpt::MinMax(1, 5), // exp
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LeOpt::MinMax(-15, -0), // waf
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};
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LeOpt opt(valRegion);
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LeOpt opt(valRegion);
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switch(mode) {
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case Mode::FAST:
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opt.setPopulationSize(100);
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opt.setNumIerations(50);
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break;
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case Mode::MEDIUM:
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opt.setPopulationSize(200);
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opt.setNumIerations(100);
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break;
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case Mode::QUALITY:
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opt.setPopulationSize(1500);
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opt.setNumIerations(150);
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break;
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}
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switch(mode) {
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case Mode::FAST:
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opt.setPopulationSize(100);
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opt.setNumIerations(50);
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break;
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case Mode::MEDIUM:
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opt.setPopulationSize(200);
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opt.setNumIerations(100);
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break;
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case Mode::QUALITY:
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opt.setPopulationSize(1500);
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opt.setNumIerations(150);
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break;
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}
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// error function
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auto func = [&] (const float* params) {
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return getErrorLogDistCeiling(mac, entries, params, nullptr);
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};
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||||
// error function
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auto func = [&] (const float* params) {
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return getErrorLogDistCeiling(mac, entries, params, nullptr);
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};
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||||
|
||||
opt.calculateOptimum(func, (float*) ¶ms);
|
||||
opt.calculateOptimum(func, (float*) ¶ms);
|
||||
|
||||
// using LeOpt = K::NumOptAlgoGenetic<float>;
|
||||
// LeOpt opt(6);
|
||||
// opt.setPopulationSize(750);
|
||||
// opt.setMaxIterations(50);
|
||||
// opt.setElitism(0.05f);
|
||||
// opt.setMutation(0.75f);
|
||||
// //opt.setValRange({0.5, 0.5, 0.5, 0.1, 0.1, 0.1});
|
||||
// opt.setValRegion(valRegion);
|
||||
// using LeOpt = K::NumOptAlgoGenetic<float>;
|
||||
// LeOpt opt(6);
|
||||
// opt.setPopulationSize(750);
|
||||
// opt.setMaxIterations(50);
|
||||
// opt.setElitism(0.05f);
|
||||
// opt.setMutation(0.75f);
|
||||
// //opt.setValRange({0.5, 0.5, 0.5, 0.1, 0.1, 0.1});
|
||||
// opt.setValRegion(valRegion);
|
||||
|
||||
// K::NumOptAlgoDownhillSimplex<float, 6> opt;
|
||||
// opt.setMaxIterations(100);
|
||||
// opt.setNumRestarts(10);
|
||||
// K::NumOptAlgoDownhillSimplex<float, 6> opt;
|
||||
// opt.setMaxIterations(100);
|
||||
// opt.setNumRestarts(10);
|
||||
|
||||
opt.calculateOptimum(func, (float*) ¶ms);
|
||||
res.error_db = getErrorLogDistCeiling(mac, entries, (float*)¶ms, &res);
|
||||
res.usedFingerprins = entries.size();
|
||||
opt.calculateOptimum(func, (float*) ¶ms);
|
||||
res.error_db = getErrorLogDistCeiling(mac, entries, (float*)¶ms, &res);
|
||||
res.usedFingerprins = entries.size();
|
||||
|
||||
Log::tock();
|
||||
Log::add(name, mac.asString() + ": " + params.asString() + " @ " + std::to_string(res.error_db) +" dB err");
|
||||
Log::tock();
|
||||
Log::add(name, mac.asString() + ": " + params.asString() + " @ " + std::to_string(res.error_db) +" dB err");
|
||||
|
||||
return params;
|
||||
return params;
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
private:
|
||||
private:
|
||||
|
||||
float getErrorLogDistCeiling(const MACAddress& mac, const std::vector<RSSIatPosition>& entries, const float* data, Stats* stats = nullptr) const {
|
||||
float getErrorLogDistCeiling(const MACAddress& mac, const std::vector<RSSIatPosition>& entries, const float* data, Stats* stats = nullptr) const {
|
||||
|
||||
const APParams* params = (APParams*) data;
|
||||
const APParams* params = (APParams*) data;
|
||||
|
||||
// some sanity checks
|
||||
if (params->outOfRange()) {return 1e10;}
|
||||
// some sanity checks
|
||||
if (params->outOfRange()) {return 1e10;}
|
||||
|
||||
// current position guess for the AP;
|
||||
const Point3 apPos_m = params->getPos();
|
||||
// current position guess for the AP;
|
||||
const Point3 apPos_m = params->getPos();
|
||||
|
||||
// add the AP [described by the current guess] to the signal-strength-prediction model
|
||||
// signal-strength-prediction-model...
|
||||
WiFiModelLogDistCeiling model(map);
|
||||
model.addAP(mac, WiFiModelLogDistCeiling::APEntry(apPos_m, params->txp, params->exp, params->waf));
|
||||
// add the AP [described by the current guess] to the signal-strength-prediction model
|
||||
// signal-strength-prediction-model...
|
||||
WiFiModelLogDistCeiling model(map);
|
||||
model.addAP(mac, WiFiModelLogDistCeiling::APEntry(apPos_m, params->txp, params->exp, params->waf));
|
||||
|
||||
float err = 0;
|
||||
int cnt = 0;
|
||||
float err = 0;
|
||||
int cnt = 0;
|
||||
|
||||
// process each measurement
|
||||
for (const RSSIatPosition& reading : entries) {
|
||||
// process each measurement
|
||||
for (const RSSIatPosition& reading : entries) {
|
||||
|
||||
// get the model-estimation for the fingerprint's position
|
||||
const float rssiModel = model.getRSSI(mac, reading.pos_m);
|
||||
// get the model-estimation for the fingerprint's position
|
||||
const float rssiModel = model.getRSSI(mac, reading.pos_m);
|
||||
|
||||
// difference between estimation and measurement
|
||||
const float diff = std::abs(rssiModel - reading.rssi);
|
||||
// difference between estimation and measurement
|
||||
const float diff = std::abs(rssiModel - reading.rssi);
|
||||
|
||||
// add error to stats object?
|
||||
if (stats) {
|
||||
stats->errors.push_back(ErrorAtPosition(reading.pos_m, reading.rssi, rssiModel));
|
||||
}
|
||||
// add error to stats object?
|
||||
if (stats) {
|
||||
stats->errors.push_back(ErrorAtPosition(reading.pos_m, reading.rssi, rssiModel));
|
||||
}
|
||||
|
||||
// adjust the error
|
||||
err += std::pow(std::abs(diff), 2.0);
|
||||
++cnt;
|
||||
// adjust the error
|
||||
err += std::pow(std::abs(diff), 2.0);
|
||||
++cnt;
|
||||
|
||||
// max distance penality
|
||||
// [unlikely to get a reading for this AP here!]
|
||||
if (apPos_m.getDistance(reading.pos_m) > 150) {err += 999999;}
|
||||
// max distance penality
|
||||
// [unlikely to get a reading for this AP here!]
|
||||
if (apPos_m.getDistance(reading.pos_m) > 150) {err += 999999;}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
err /= cnt;
|
||||
err = std::sqrt(err);
|
||||
err /= cnt;
|
||||
err = std::sqrt(err);
|
||||
|
||||
if (params->txp < -50) {err += 999999;}
|
||||
if (params->txp > -35) {err += 999999;}
|
||||
if (params->txp < -65) {err += 999999;}
|
||||
if (params->txp > -45) {err += 999999;}
|
||||
|
||||
if (params->exp > 3.5) {err += 999999;}
|
||||
if (params->exp < 1.0) {err += 999999;}
|
||||
if (params->exp > 5) {err += 999999;}
|
||||
if (params->exp < 1.0) {err += 999999;}
|
||||
|
||||
return err;
|
||||
return err;
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
};
|
||||
};
|
||||
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user