㈠ 無線感測器網路的定位演算法的發展歷史
這個問題問的太難了...這可以寫一片碩士論文了。沒有人會研究那麼廣的。只能說先是靜態的定位，然後AD HOC網路也需要定位，那就出現了動態的。
目前來說，WSN的定位還是主要研究靜態網路的定位。其方法從傳統意義來講分為：基於測距的定位演算法和非基於測距的定位演算法。以我推斷，應該是基於測距的定位方法出現在先。
具體分類及方法接受你可以參照《location，localization，and localizability》第一作者：劉雲浩。該文章出自英文版的《計算機科學與技術》希望能幫助你。
㈡ 高德地圖網路定位原理
網路定位是根據你當前IP確定大概位置，GPS是實時定位追蹤，根據手機內置GPS晶元連接衛星實時定位
㈢ 無線網路定位演算法的程序。TOA/RSSI什麼演算法的都可以，想找個程序作個參考。
我有matlab的。
太多了，我的QQ：39400877
%|
%| SCRIPT: simMLE
%|
%| PURPOSE: Simulate a relative location system by generating
%| random measurements and maximizing the likelihood fcn.
%| After many trials, show the results vs. the Cramer-Rao Bound.
%|
%| AUTHOR: Neal Patwari
%| http://www.engin.umich.e/~npatwari/
%|
%| REFERENCE: Relative Location Estimation in Wireless Sensor Networks
%| (N. Patwari, A. O. Hero, M. Perkins, N. S. Correal, R. J. O'Dea),
%| IEEE Trans. Signal Processing, vol. 51, no. 8, Aug. 2003, pp. 2137-2148.
%|
tic
% Use globals to allow minimization functions access to network info,
% debugging info.
global refDevices blindDevices totalDevices linearRefLocs dhat funcEvals dfuncEvals;
% Basic simulation parameters
roomSize = [1,1]; % Room size, meters
gridSize = 5; % How many sensors per side
refDevices = 4; % How many references (must be same length as actualRefLocs)
trials = 20; % How many indep trials to run
measMethod = 'R'; % Use 'R' for RSS, 'T' for TOA
totalDevices = gridSize^2;
blindDevices = totalDevices - refDevices;
blindCoords = 2*blindDevices;
actualRefLocs = [0,0; 0,1; 1,1; 1,0];
linearRefLocs = [actualRefLocs(:,1)', actualRefLocs(:,2)'];
% Optimization parameters
ftol = 0.00001;
if measMethod == 'R',
func = 'calcError'; % Use for RSS
dfunc = 'calcDError'; % Use for RSS
else
func = 'calcErrorTOA'; % Use for TOA
dfunc = 'calcDErrorTOA'; % Use for TOA
end
%| 1. Set up the blindfolded device locations
delta = 1/(gridSize-1);
coords = 0:delta:1;
xMatrix = ones(gridSize,1)*coords;
yMatrix = xMatrix';
xBlind = [xMatrix(2:gridSize-1), ...
xMatrix(gridSize+1:totalDevices-gridSize), ...
xMatrix(totalDevices-gridSize+2:totalDevices-1)];
yBlind = [yMatrix(2:gridSize-1), ...
yMatrix(gridSize+1:totalDevices-gridSize), ...
yMatrix(totalDevices-gridSize+2:totalDevices-1)];
actualBlindLocs = [xBlind', yBlind'];
actualAllLocs = [actualRefLocs; actualBlindLocs];
xActual = actualAllLocs(:,1)';
yActual = actualAllLocs(:,2)';
actualDist = L2_distance(actualAllLocs', actualAllLocs',0);
%| 2. Define the channel model
if measMethod == 'R';
sigmaOverN = 1.7;
% If C==1, then this simulation runs the _true_ MLE.
% If C==exp( 0.5* (log(10)/10 *sigmaOverN)^2), then this runs a
% bias-corrected (pseudo-) MLE.
% C = exp( 0.5* (log(10)/10 *sigmaOverN)^2);
C = 1;
else
sigma_d = 0.2; % Use for TOA
end
for trial = 1:trials,
if measMethod == 'R';
%| 3.0 Generate a random set of RSS-based distance measurements. When RSS
%| is expressed in dB, errors are Gaussian. Here, dhat is an interim
%| variable which has units of distance, and represents an estimate for
%| the range. It is correctly randomly generated as follows:
dhat = actualDist.*10.^(sigmaOverN/10 .*symrandn(totalDevices))./C;
else
%| 3.1 Generate a set of TOA measurements, which are Gaussian around the
%| true value with variance sigma_d.
dhat = actualDist + sigma_d .* symrandn(totalDevices);
end
%| 4. Make an initial guess of the coordinates.
blindLocs0 = [xBlind, yBlind]; % Use the true coordinates (unrealistic but best case)
%| 5. Find optimum locations of neurfons (fixed and relative)
funcEvals = 0; dfuncEvals = 0;
[coordsMLE, iter, errorMin] = frprmn(blindLocs0, ftol, func, dfunc, 0);
disp(sprintf('%d: Function / Deriv. evals: %d / %d.', trial, funcEvals, dfuncEvals));
%| 6. Save the resulting estimated coords
coordEsts(trial, 1:blindCoords) = coordsMLE;
end % for trial
estMean = mean(coordEsts);
estCov = cov(coordEsts);
estVars = diag(estCov);
estStds = sqrt(estVars);
locVars = estVars(1:blindDevices) + estVars((blindDevices+1):(2*blindDevices));
locStd = sqrt(locVars);
toc % show time of execution
% Plot the location estimates for sensors, one at a time.
if 0,
figure
for i=1:blindDevices,
clf
plot(coordEsts(:,i), coordEsts(:,blindDevices+i),'.', ...
estMean(i), estMean(blindDevices+i), 'ro')
hold on
set(gca,'xlim',[-0.2 1.2])
set(gca,'ylim',[-0.2 1.2])
set(gca,'FontSize',20)
set(gca,'DataAspectRatio',[1 1 1])
xlabel('X Position (m)')
ylabel('Y Position (m)')
set(gca,'xTick',0:0.25:1)
set(gca,'yTick',0:0.25:1)
grid;
pause;
end
end
% Calculate and plot CRB vs. estimator performance.
figure; clf;
if measMethod == 'R';
[locstdCRB, coordCRB] = calcLocalizationCRB('R', [xBlind, actualRefLocs(:,1)'], ...
[yBlind, actualRefLocs(:,2)'], blindDevices, totalDevices, sigmaOverN);
else
[locstdCRB, coordCRB] = calcLocalizationCRB('T', [xBlind, actualRefLocs(:,1)'], ...
[yBlind, actualRefLocs(:,2)'], blindDevices, totalDevices, sigma_d);
end
for i=1:blindDevices,
hold on
R = cov(coordEsts(:,i), coordEsts(:,blindDevices+i));
drawOval(estMean(i), estMean(blindDevices+i), R, 'k-','v', 8, 0, 1);
R_CRB = coordCRB([i, i+blindDevices],[i, i+blindDevices]);
drawOval(xBlind(i), yBlind(i), R_CRB, 'r--','.',20, 0, 1);
end
set(gca,'xlim',[-0.2 1.2])
set(gca,'ylim',[-0.2 1.2])
set(gca,'FontSize',18)
set(gca,'DataAspectRatio',[1 1 1])
xlabel('X Position (m)')
ylabel('Y Position (m)')
set(gca,'xTick',0:0.25:1)
set(gca,'yTick',0:0.25:1)
grid;
% Use for comparison
RMS_est_Std = sqrt(mean(locStd.^2))
RMS_crb_Std = sqrt(mean(locstdCRB.^2))
㈣ 網路定位跟GPS定位有什麼區別
回答：網路定位的共同特點是速度快，只要聯網，瞬間可以定位，任何手機都可以秒定，網路定位有兩種方式，一種是WIFI小區定位，是根據WIFI路由器所在位置進行定位，這種定位精度比較高，但不可靠。GPS是一個與手機、電腦都無關的東西，其名為全球定位系統，是利用衛星精確確定設備所在位置的功能，一般利用電腦軟體，配合精確的地圖使用。
㈤ 網路定位是什麼意思
通過wifi或者手機數據網路來確定你的位置
精確度有點差 wifi應該精確到五米左右
手機網路的話看周圍信號塔，一般在十米到1000米之間
㈥ wifi網路定位原理是怎樣的
WiFi網路定位需要採集wifi接入點的位置信息。wifi熱點越來越多，在城市中更趨向於空間任何一點都能接收到至少一個AP的信號。熱點只要通電，都一定會向周圍發射信號。信號中包含此熱點的唯一全球ID。即使距離此熱點比較遠，無法建立連接，但還是可以偵聽到它的存在。
熱點一般都是很少變位置的，比較固定。定位端只要偵聽一下附近都有哪些熱點，檢測一下每個熱點的信號強弱，然後把這些信息發送給Skyhook的伺服器。伺服器根據這些信息，查詢每個熱點在資料庫里記錄的坐標，進行運算，就能知道客戶端的具體位置了，再把坐標告訴客戶端。只要收到的AP信號越多，定位就會越准。
㈦ 請問pc網路定位原理是什麼原理
就是通過室內布置若干個已知坐標的定位基站，需要定位的人員攜帶定位標簽，標簽按照一定的頻率發射脈沖，不斷和已知位置的基站進行測距，通過一定的精確演算法定出標簽的位置！